Journal of Marine Sciences ›› 2019, Vol. 37 ›› Issue (4): 1-13.DOI: 10.3969/j.issn.1001-909X.2019.04.001.

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Assessment of intraseasonal variabilities over Indian Ocean based on oceanic reanalysis datasets

MENG Ze1,2,3, ZHOU Lei*3, QIN Jian-huang3, FU Hong-li4, WANG Guan-suo5   

  1. 1. Second Institute of Oceanography, MNR, Hangzhou 310012, China;
    2. State Key Laboratory of Satellite Ocean Environment Dynamics, Hangzhou 310012, China;
    3. School of Oceanography, Shanghai Jiao Tong University, Shanghai 200030, China;
    4. National Marine Data and Information Service, Tianjin 300171, China;
    5. Lab of Marine Science and Numerical Modeling, First Institute of Oceanography, MNR, Qingdao 266061, China
  • Received:2019-03-26 Revised:2019-05-22 Online:2019-12-15 Published:2022-11-10

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Abstract: Intraseasonal variabilities (ISVs) are important factors in tropical climate. The ISVs like sea surface temperature (SST) and sea surface height (SSH) from oceanic reanalysis ECCO2, SODA3 and CORA were compared with the satellite observations during boreal summer and winter. Intraseasonal SST anomalies were also further compared during MJO and CIO cases. Results show that the ISVs from reanalysis data have the same clear variabilities nearshore as the observations, while within the ocean, the ISVs standard deviation (STD) of reanalysis data is weaker than that of the observation data for at least 20%, SODA3 even faces the differences up to 60%. During the eastward propagation of MJO and northward propagation of CIO, the SST anomalies induced by thermal force is well simulated by the reanalysis data, only ECCO2 and CORA encounter a phase lag of 5-10 days. During the westward propagation of SST anomalies at CIO, reanalysis data have an awful simulation for this dynamic forced ISVs. ECCO2 and CORA propagate to the west in a small area from 85°E-95°E feebly, and even that in SODA3 doesn’t show its westward signals. Temperature in all reanalysis data is a half weaker than the observations at eastern Indian Ocean (90°E-100°E). Through comparison in intraseasonal velocity anomalies, the STD of reanalysis data is far less than RAMA (51.42%), the averages among the peaks of velocity are also weaker than observations for 65.16%. This suggests that improper simulation of dynamically-forcing ISVs may lead to the weaker variabilities within Indian Ocean. Therefore, in order to upgrade the ISVs in reanalysis, it is necessary to modify the heat forcing as well as dynamic forcing in atmospheric modeling and, more importantly, add up the oceanic assimilation. Equatorial Indian Ocean, as the prevalent area for ISV events and the area with clear difference in STD, is surely a region for more observation plans with oceanic variabilities like currents.

Key words: Indian Ocean, intraseasonal variability, Madden-Julian Oscillation, Central Indian Ocean mode, ocean reanalysis datasets

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